Automated Author ProfileGüttel, Licinia
https://osf.io/k2thp
Güttel, Licinia
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The German AI policy dataset was compiled based on an examination of the OECD AI Observatory Dashboard (OECD AI Policy Observatory, 2023) and a detailed search on websites of German government bodies. This search was conducted using the keywords "KI" (AI) and "Künstliche Intelligenz" (Artificial Intelligence), which appear in the title or text of a document. The government websites included all German ministries that were active during the time frame from 2012 until 2021, as well as the Bundestag (German parliament). The dataset was created in the context of the international research project "Shaping 21st Century AI – Controversies and Closure in Media, Policy, and Research".
Authors
- Valenti, Davide ;
- Katzenbach, Christian ;
- Seliger, Oke ;
- Liebig, Laura ;
- Güttel, Licinia ;
- Jobin, Anna